pix2pixHD---model---辨别器

搭建完生成器后搭建辨别器。
首先看辨别器的输入：分别是标签和生成器输出。

在训练时候，辨别器通道输入等于生成器的输出加上conditional即标签和实例的拼接。通道相加就是图片concat。

如果使用实例图片，那么辨别器输入通道数加1，通过调用networks文件的define_D看辨别器设计：
首先是一些参数：


辨别器是多尺度辨别器：

class MultiscaleDiscriminator(nn.Module):
    def __init__(self, input_nc, ndf=64, n_layers=3, norm_layer=nn.BatchNorm2d, 
                 use_sigmoid=False, num_D=3, getIntermFeat=False):
        super(MultiscaleDiscriminator, self).__init__()
        self.num_D = num_D
        self.n_layers = n_layers
        self.getIntermFeat = getIntermFeat
     
        for i in range(num_D):
            netD = NLayerDiscriminator(input_nc, ndf, n_layers, norm_layer, use_sigmoid, getIntermFeat)
            if getIntermFeat:                                
                for j in range(n_layers+2):
                    setattr(self, 'scale'+str(i)+'_layer'+str(j), getattr(netD, 'model'+str(j)))                                   
            else:
                setattr(self, 'layer'+str(i), netD.model)

        self.downsample = nn.AvgPool2d(3, stride=2, padding=[1, 1], count_include_pad=False)

    def singleD_forward(self, model, input):
        if self.getIntermFeat:
            result = [input]
            for i in range(len(model)):
                result.append(model[i](result[-1]))
            return result[1:]
        else:
            return [model(input)]

    def forward(self, input):        
        num_D = self.num_D
        result = []
        input_downsampled = input
        for i in range(num_D):
            if self.getIntermFeat:
                model = [getattr(self, 'scale'+str(num_D-1-i)+'_layer'+str(j)) for j in range(self.n_layers+2)]
            else:
                model = getattr(self, 'layer'+str(num_D-1-i))
            result.append(self.singleD_forward(model, input_downsampled))
            if i != (num_D-1):
                input_downsampled = self.downsample(input_downsampled)
        return result

先看forward函数：getIntermFeat等于True，接下来有getattr获得属性，那前面肯定有setattr设置属性。




获得scale2_layer0/1/2/3/4和scale1_layer0/1/2/3/4和scale2_layer0/1/2/3/4对应的属性：

这里分两步：
首先getattr(netD, ‘model’+str(j)))获得netD对应的model0/1/2/3/4对应的属性。

# Defines the PatchGAN discriminator with the specified arguments.
class NLayerDiscriminator(nn.Module):
    def __init__(self, input_nc, ndf=64, n_layers=3, norm_layer=nn.BatchNorm2d, use_sigmoid=False, getIntermFeat=False):
        super(NLayerDiscriminator, self).__init__()
        self.getIntermFeat = getIntermFeat
        self.n_layers = n_layers

        kw = 4
        padw = int(np.ceil((kw-1.0)/2))
        sequence = [[nn.Conv2d(input_nc, ndf, kernel_size=kw, stride=2, padding=padw), nn.LeakyReLU(0.2, True)]]

        nf = ndf
        for n in range(1, n_layers):
            nf_prev = nf
            nf = min(nf * 2, 512)
            sequence += [[
                nn.Conv2d(nf_prev, nf, kernel_size=kw, stride=2, padding=padw),
                norm_layer(nf), nn.LeakyReLU(0.2, True)
            ]]

        nf_prev = nf
        nf = min(nf * 2, 512)
        sequence += [[
            nn.Conv2d(nf_prev, nf, kernel_size=kw, stride=1, padding=padw),
            norm_layer(nf),
            nn.LeakyReLU(0.2, True)
        ]]

        sequence += [[nn.Conv2d(nf, 1, kernel_size=kw, stride=1, padding=padw)]]

        if use_sigmoid:
            sequence += [[nn.Sigmoid()]]

        if getIntermFeat:
            for n in range(len(sequence)):
                setattr(self, 'model'+str(n), nn.Sequential(*sequence[n]))
        else:
            sequence_stream = []
            for n in range(len(sequence)):
                sequence_stream += sequence[n]
            self.model = nn.Sequential(*sequence_stream)

    def forward(self, input):
        if self.getIntermFeat:
            res = [input]
            for n in range(self.n_layers+2):
                model = getattr(self, 'model'+str(n))
                res.append(model(res[-1]))
            return res[1:]
        else:
            return self.model(input)     

获得model0/1/2/3/4属性对应的值：

去ini寻找如何设置的属性：首先看序列的长度。根据for循环设置model0等对应的属性。
序列由5个卷积，一个sigmoid组成。总长6.

接着遍历6，model0/1/2/3/4/5分别对应序列的前五个列表。

接着遍历5次，每一次取出一个操作对应于model，然后将生成的结果放在列表中，第一个是input，后面的依次排放，然后取上一次处理的结果作为输出，就这样处理五次，列表中就有6个值，最后除了input其余的都输出。

辨别器就结束了。